Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features

TL;DR

This paper presents a hybrid approach combining syntactic features and transformer-based embeddings to identify and retrieve check-worthy claims in social media posts, improving automated fact-checking processes.

Contribution

It introduces a novel fusion of syntactic and transformer features for claim detection and applies semantic similarity models for claim retrieval in social media.

Findings

Effective claim check-worthiness classification for English and Arabic tweets.

Improved claim retrieval using Siamese transformer embeddings.

Detailed feature analysis demonstrating the model's strengths.

Abstract

In this digital age of news consumption, a news reader has the ability to react, express and share opinions with others in a highly interactive and fast manner. As a consequence, fake news has made its way into our daily life because of very limited capacity to verify news on the Internet by large companies as well as individuals. In this paper, we focus on solving two problems which are part of the fact-checking ecosystem that can help to automate fact-checking of claims in an ever increasing stream of content on social media. For the first problem, claim check-worthiness prediction, we explore the fusion of syntactic features and deep transformer Bidirectional Encoder Representations from Transformers (BERT) embeddings, to classify check-worthiness of a tweet, i.e. whether it includes a claim or not. We conduct a detailed feature analysis and present our best performing models for English and Arabic tweets. For the second problem, claim retrieval, we explore the pre-trained embeddings from a Siamese network transformer model (sentence-transformers) specifically trained for semantic textual similarity, and perform KD-search to retrieve verified claims with respect to a query tweet.